Promoter Identification in Daphnia Populations Revealed by Transcription Start Site Profiling
Bibliographic record
Abstract
abstract: Regulation of transcription initiation is a critical factor in the emergence of diverse biological phenotypes, including the development of multiple cell types from a single genotype, the ability of organisms to respond to environmental cues, and the rise of heritable diseases. Transcription initiation is regulated in large part by promoter regions of DNA. The identification and characterization of cis-regulatory regions, and understanding how these sequences differ across species, is a question of interest in evolution. To address this topic, I used the model organism Daphnia pulex, a well-characterized microcrustacean with an annotated genome sequence and selected a distribution of well-defined populations geographically located throughout the Midwestern US, Oregon, and Canada. Using isolated total RNA from adult, female Daphnia originating from the selected populations as well as a related taxon, Daphnia pulicaria (200,000 years diverged from D. pulex), I identified an average of over 14,000 (n=14,471) promoter regions using a novel transcription start site (TSS) profiling method, STRIPE-seq. Through the identification of sequence architecture, promoter class, conservation, and transcription start region (TSR) width, of cis-regulatory regions across the aforementioned Daphnia populations, I constructed a system for the study of promoter evolution, enabling a robust interpretation of promoter evolution in the context of the population-genetic environment. The methodology presented, coupled with the generated dataset, provides a foundation for the study of the evolution of promoters across both species and populations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".